NDLI: Semi-supervised learning using label mean

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Archipelago: nonparametric Bayesian semi-supervised learning

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

Route kernels for trees

Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

Grammatical inference as a principal component analysis problem

Curriculum learning

Importance weighted active learning

Split variational inference

Predictive representations for policy gradient in POMDPs

Online feature elicitation in interactive optimization

Spectral clustering based on the graph p-Laplacian

Active learning for directed exploration of complex systems

Optimized expected information gain for nonlinear dynamical systems

Probabilistic dyadic data analysis with local and global consistency

Structure learning of Bayesian networks using constraints

Robust bounds for classification via selective sampling

Multi-view clustering via canonical correlation analysis

A convex formulation for learning shared structures from multiple tasks

Learning kernels from indefinite similarities

Matrix updates for perceptron training of continuous density hidden Markov models

Decision tree and instance-based learning for label ranking

Learning dictionaries of stable autoregressive models for audio scene analysis

Exploiting sparse Markov and covariance structure in multiresolution models

Nonparametric estimation of the precision-recall curve

EigenTransfer: a unified framework for transfer learning

Fitting a graph to vector data

Unsupervised search-based structured prediction

Deep transfer via second-order Markov logic

Analytic moment-based Gaussian process filtering

Good learners for evil teachers

A scalable framework for discovering coherent co-clusters in noisy data

The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning

Proximal regularization for online and batch learning

Large margin training for hidden Markov models with partially observed states

Accelerated sampling for the Indian Buffet Process

Accounting for burstiness in topic models

Domain adaptation from multiple sources via auxiliary classifiers

Boosting with structural sparsity

Learning to segment from a few well-selected training images

GAODE and HAODE: two proposals based on AODE to deal with continuous variables

A majorization-minimization algorithm for (multiple) hyperparameter learning

Dynamic mixed membership blockmodel for evolving networks

Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property

Sequential Bayesian prediction in the presence of changepoints

PAC-Bayesian learning of linear classifiers

Fast evolutionary maximum margin clustering

Dynamic analysis of multiagent Q-learning with ε-greedy exploration

Bayesian inference for Plackett-Luce ranking models

Bayesian clustering for email campaign detection

Efficient learning algorithms for changing environments

Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search

Partially supervised feature selection with regularized linear models

Learning with structured sparsity

Learning linear dynamical systems without sequence information

Group lasso with overlap and graph lasso

Graph construction and b-matching for semi-supervised learning

Trajectory prediction: learning to map situations to robot trajectories

An accelerated gradient method for trace norm minimization

A novel lexicalized HMM-based learning framework for web opinion mining

Orbit-product representation and correction of Gaussian belief propagation

A Bayesian approach to protein model quality assessment

Learning prediction suffix trees with Winnow

Boosting products of base classifiers

Learning Markov logic network structure via hypergraph lifting

Near-Bayesian exploration in polynomial time

Regularization and feature selection in least-squares temporal difference learning

The graphlet spectrum

Rule learning with monotonicity constraints

Multiple indefinite kernel learning with mixed norm regularization

On sampling-based approximate spectral decomposition

Learning spectral graph transformations for link prediction

Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties

Generalization analysis of listwise learning-to-rank algorithms

Approximate inference for planning in stochastic relational worlds

Learning nonlinear dynamic models

Non-linear matrix factorization with Gaussian processes

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Transfer learning for collaborative filtering via a rating-matrix generative model

ABC-boost: adaptive base class boost for multi-class classification

Semi-supervised learning using label mean

Learning from measurements in exponential families

Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery

Efficient Euclidean projections in linear time

Topic-link LDA: joint models of topic and author community

Geometry-aware metric learning

Identifying suspicious URLs: an application of large-scale online learning

Online dictionary learning for sparse coding

Proto-predictive representation of states with simple recurrent temporal-difference networks

Sparse Gaussian graphical models with unknown block structure

Polyhedral outer approximations with application to natural language parsing

Partial order embedding with multiple kernels

Bandit-based optimization on graphs with application to library performance tuning

Deep learning from temporal coherence in video

Regression by dependence minimization and its application to causal inference in additive noise models

Learning complex motions by sequencing simpler motion templates

Convex variational Bayesian inference for large scale generalized linear models

Solution stability in linear programming relaxations: graph partitioning and unsupervised learning

Nonparametric factor analysis with beta process priors

Unsupervised hierarchical modeling of locomotion styles

Binary action search for learning continuous-action control policies

Detecting the direction of causal time series

Constraint relaxation in approximate linear programs

Multi-class image segmentation using conditional random fields and global classification

Learning when to stop thinking and do something!

Independent factor topic models

An efficient sparse metric learning in high-dimensional space via $l_{1}-penalized$ log-determinant regularization

Sparse higher order conditional random fields for improved sequence labeling

An efficient projection for $l_{1},$ $_{∞}$ regularization

Nearest neighbors in high-dimensional data: the emergence and influence of hubs

Large-scale deep unsupervised learning using graphics processors

The Bayesian group-Lasso for analyzing contingency tables

Supervised learning from multiple experts: whom to trust when everyone lies a bit

Surrogate regret bounds for proper losses

Learning structurally consistent undirected probabilistic graphical models

Ranking interesting subgroups

Function factorization using warped Gaussian processes

Stochastic methods for $l_{1}$ regularized loss minimization

Structure preserving embedding

Monte-Carlo simulation balancing

Uncertainty sampling and transductive experimental design for active dual supervision

Hilbert space embeddings of conditional distributions with applications to dynamical systems

Multi-assignment clustering for Boolean data

A least squares formulation for a class of generalized eigenvalue problems in machine learning

A simpler unified analysis of budget perceptrons

Fast gradient-descent methods for temporal-difference learning with linear function approximation

Optimistic initialization and greediness lead to polynomial time learning in factored MDPs

Discriminative k-metrics

Kernelized value function approximation for reinforcement learning

Factored conditional restricted Boltzmann Machines for modeling motion style

Using fast weights to improve persistent contrastive divergence

Structure learning with independent non-identically distributed data

Robot trajectory optimization using approximate inference

Ranking with ordered weighted pairwise classification

More generality in efficient multiple kernel learning

Information theoretic measures for clusterings comparison: is a correction for chance necessary?

Model-free reinforcement learning as mixture learning

BoltzRank: learning to maximize expected ranking gain

K-means in space: a radiation sensitivity evaluation

Evaluation methods for topic models

Feature hashing for large scale multitask learning

Herding dynamical weights to learn

A stochastic memoizer for sequence data

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning

Non-monotonic feature selection

Online learning by ellipsoid method

Stochastic search using the natural gradient

Learning structural SVMs with latent variables

Piecewise-stationary bandit problems with side observations

Large-scale collaborative prediction using a nonparametric random effects model

Robust feature extraction via information theoretic learning

Interactively optimizing information retrieval systems as a dueling bandits problem

Compositional noisy-logical learning

Discovering options from example trajectories

Learning instance specific distances using metric propagation

Prototype vector machine for large scale semi-supervised learning

Learning non-redundant codebooks for classifying complex objects

Multi-instance learning by treating instances as non-I.I.D. samples

MedLDA: maximum margin supervised topic models for regression and classification

On primal and dual sparsity of Markov networks

SimpleNPKL: simple non-parametric kernel learning

Invited talk: Can learning kernels help performance?

Invited talk: Drifting games, boosting and online learning

Workshop summary: Seventh annual workshop on Bayes applications

Workshop summary: Automated interpretation and modelling of cell images

Workshop summary: Workshop on learning feature hierarchies

Workshop summary: Results of the 2009 reinforcement learning competition

Workshop summary: The fourth workshop on evaluation methods for machine learning

Workshop summary: On-line learning with limited feedback

Workshop summary: Numerical mathematics in machine learning

Workshop summary: Abstraction in reinforcement learning

Workshop summary: Sparse methods for music audio

Tutorial summary: Reductions in machine learning

Tutorial summary: Convergence of natural dynamics to equilibria

Tutorial summary: Learning with dependencies between several response variables

Tutorial summary: Survey of boosting from an optimization perspective

Tutorial summary: The neuroscience of reinforcement learning

Tutorial summary: Machine learning in IR: recent successes and new opportunities

Tutorial summary: Active learning

Tutorial summary: Large social and information networks: opportunities for ML

Tutorial summary: Structured prediction for natural language processing

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